Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

IVIM parameters mapping with artificial neural network based on mean deviation prior

BACKGROUND

The diffusion and perfusion parameters derived from intravoxel incoherent motion (IVIM) imaging provide promising biomarkers for noninvasively quantifying and managing various diseases. Nevertheless, due to the distribution gap between simulated and real datasets, the out-of-distribution (OOD) problem occurred in supervised learning-based methods degrades their performance and hinders their real applications.

PURPOSE

To address the OOD problem in supervised methods and to further improve the accuracy and stability of IVIM parameter estimation, this work proposes a novel learning framework called IterANN, based on mean deviation prior (MDP) between training and estimated IVIM parameters on the test set.

METHODS

Specifically, MDP indicates that the mean of the estimated IVIM parameters always locates between the mean of IVIM parameters in the test and train sets. In IterANN, we adopt a very simple artificial neural network (ANN) architecture of two hidden layers with 12 neurons per hidden layer, an input layer containing the signals acquired at multiple b-values and an output layer composed of three IVIM parameters ( D $D$ , F $F$ andD S t a r $DStar$ ). Inspired by MDP, the distribution of IVIM parameters in the training set (simulated data) is iteratively updated so that their mean gradually approaches the predicted values of the real data. This aims to achieve a strong correlation between the simulated data and the real data. To validate the effectiveness of IterANN, we compare it with several methods on both simulation and real acquisition datasets, including 21 healthy and 3 tumor subjects, in terms of residual errors of IVIM parameters or DW signals, the coefficients of variation (CV) of IVIM parameters, and the parameter contrast-to-noise ratio (PCNR) between normal and tumor tissues.

RESULTS

On two simulation datasets, the proposed IterANN achieves the lowest residual error in IVIM parameters, especially in the case of low signal-to-noise ratio (SNR = 10), the residual error of D $D$ , F $F$ andD S t a r $DStar$ is decreased by15.82 % / 14.92 % , 81.19 % / 74.04 % , 50.77 % / 1.549 % $15.82\%/14.92\%, 81.19\%/74.04\%, 50.77\%/1.549\%$ (Gaussian distribution /realistic distribution) respectively comparing to the suboptimal method. On real dataset, the IterANN achieves the highest PCNR when comparing the normal and tumor regions. Additionally, the proposed IterANN demonstrated better stability, with its CV being significantly lower than that of other methods in the vast majority of cases (p < 0.01 $p<0.01$ , paired-sample Student's t-test).

CONCLUSIONS

The superior performance of IterANN demonstrates that updating the distribution of the train set based on MDP can effectively solve the OOD problem, which allows us not only to improve the accuracy and stability of the estimated IVIM parameters, but also to increase the potential of IVIM in disease diagnosis.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data

PURPOSE

Joint analysis of flow-compensated (FC) and non-flow-compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain.

METHODS

Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non-linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b-values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning-based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b-values 0-200 s/mm2 and corresponding flow weighting factors 0-2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis.

RESULTS

Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning-based algorithm for IVIM parametersD $$ D $$ andf $$ f $$ , and for the Bayesian algorithm only forv d $$ {v}_d $$ , relative to the other methods.

CONCLUSION

A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning-based algorithms appear promising.

Multisite MRI Intravoxel Incoherent Motion Repeatability and Reproducibility across 3 T Scanners in a Breast Diffusion Phantom: A BReast Intravoxel Incoherent Motion Multisite (BRIMM) Study

BACKGROUND

Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging is helpful in the characterization of breast tumors. However, repeatability/reproducibility studies across scanners and across sites are scarce.

PURPOSE

To evaluate the repeatability and reproducibility of ADC and IVIM parameters (tissue diffusivity (Dt), perfusion fraction (Fp) and pseudo-diffusion (Dp)) within and across sites employing MRI scanners from different vendors utilizing 16-channel breast array coils in a breast diffusion phantom.

STUDY TYPE

Phantom repeatability.

PHANTOM

A breast phantom containing tubes of different polyvinylpyrrolidone (PVP) concentrations, water, fat, and sponge flow chambers, together with an MR-compatible liquid crystal (LC) thermometer.

FIELD STRENGTH/SEQUENCE

Bipolar gradient twice-refocused spin echo sequence and monopolar gradient single spin echo sequence at 3 T.

ASSESSMENT

Studies were performed twice in each of two scanners, located at different sites, on each of 2 days, resulting in four studies per scanner. ADCs of the PVP and water were normalized to the vendor-provided calibrated values at the temperature indicated by the LC thermometer for repeatability/reproducibility comparisons.

STATISTICAL TESTS

ADC and IVIM repeatability and reproducibility within and across sites were estimated via the within-system coefficient of variation (wCV). Pearson correlation coefficient (r) was also computed between IVIM metrics and flow speed. A P value <0.05 was considered statistically significant.

RESULTS

ADC and Dt demonstrated excellent repeatability (<2%; <3%, respectively) and reproducibility (both <5%) at the two sites. Fp and Dp exhibited good repeatability (mean of two sites 3.67% and 5.59%, respectively) and moderate reproducibility (mean of two sites 15.96% and 13.3%, respectively). The mean intersite reproducibility (%) of Fp/Dp/Dt was 50.96/13.68/5.59, respectively. Fp and Dt demonstrated high correlations with flow speed while Dp showed lower correlations. Fp correlations with flow speed were significant at both sites.

DATA CONCLUSION

IVIM reproducibility results were promising and similar to ADC, particularly for Dt. The results were reproducible within both sites, and a progressive trend toward reproducibility across sites except for Fp.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Assessment of fitting methods and variability of IVIM parameters in muscles of the lumbar spine at rest

Intravoxel incoherent motion (IVIM) MRI provides insight into tissue diffusion and perfusion. Here, estimates of perfusion fraction ( f ), pseudo-diffusion coefficient (D * ), and diffusion coefficient ( D ) obtained via different fitting methods are compared to ascertain (1) the optimal analysis strategy for muscles of the lumbar spine and (2) repeatability of IVIM parameters in skeletal muscle at rest. Diffusion-weighted images were acquired in the lumbar spine at rest in 15 healthy participants. Data were fit to the bi-exponential IVIM model to estimate f , D * and D using three variably segmented approaches based on non-linear least squares fitting, and a Bayesian fitting method. Assuming that perfusion and diffusion are temporally stable in skeletal muscle at rest, and spatially uniform within a spinal segment, the optimal analysis strategy was determined as the approach with the lowest temporal or spatial variation and smallest residual between measured and fit data. Inter-session repeatability of IVIM parameters was evaluated in a subset of 11 people. Finally, simulated IVIM signal at varying signal to noise ratio were evaluated to understand precision and bias. Experimental results showed that IVIM parameter values differed depending on the fitting method. A three-step non-linear least squares fitting approach, where D , f , andD * were estimated sequentially, generally yielded the lowest spatial and temporal variation. Solving all parameters simultaneously yielded the lowest residual between measured and fit data, however there was substantial spatial and temporal variability. Results obtained by Bayesian fitting had high spatial and temporal variability in addition to a large residual between measured and fit data. Simulations showed that all fitting methods can fit the IVIM data at signal to noise ratios >35, and thatD * was the most challenging to accurately obtain. Overall, this study motivates use of a three-step non-linear least squares fitting strategy to quantify IVIM parameters in skeletal muscle.

Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study

Background

The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise.

Objective

This study aims to improve the accuracy of IVIM output parameters.

Material and Methods

In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method purifies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters.

Results

Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively.

Conclusion

The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.

Intravoxel Incoherent Motion Quantification Dependent on Measurement SNR and Tissue Perfusion: A Simulation Study

Background

The intravoxel incoherent motion (IVIM) model extracts both functional and structural information of a tissue using motion-sensitizing gradients.

Objective

The Objective of the present work is to investigate the impact of signal to noise ratio (SNR) and physiologic conditions on the validity of IVIM parameters.

Material and Methods

This study is a simulation study, modeling IVIM at a voxel, and also done 10,000 times for every single simulation. Complex noises with various standard deviations were added to signal in-silico to investigate SNR effects on output validity. Besides, some blood perfusion situations for different tissues were considered based on their physiological range to explore the impacts of blood fraction at each voxel on the validity of the IVIM outputs. Coefficient variation (CV) and bias of the estimations were computed to assess the validity of the IVIM parameters.

Results

This study has shown that the validity of IVIM output parameters highly depends on measurement SNR and physiologic characteristics of the studied organ.

Conclusion

IVIM imaging could be useful if imaging parameters are correctly selected for each specific organ, considering hardware limitations.

Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf

PURPOSES

The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values.

METHODS

Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm², and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and D_d, f_p, and D_p from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs.

RESULTS

The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed D_d, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only.

CONCLUSIONS

The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and D_d were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.

The quantification of IntraVoxel incoherent motion - MRI maps cannot preserve texture information: An evaluation based on simulated and in-vivo images

BACKGROUND

Radiomics can be applied on parametric maps obtained from IntraVoxel Incoherent Motion (IVIM) MRI to characterize heterogeneity in diffusion and perfusion tissue properties. The purpose of this work is to assess the accuracy and reproducibility of radiomic features computed from IVIM maps using different fitting methods.

METHODS

200 digitally simulated IVIM-MRI images with various SNR containing different combinations of texture patterns were generated from ground truth maps of true diffusion D, pseudo-diffusion D* and perfusion fraction f. Four different methods (segmented least-square LSQ, Bayesian, supervised and unsupervised deep learning DL) were adopted to quantify IVIM maps from simulations and from two real images of liver tumor. Radiomic features were computed from ground truth and estimated maps. Accuracy and reproducibility among quantification methods were assessed.

RESULTS

Almost 50% of radiomic features computed from D maps using DL approaches, 36% using Bayes and 27% using LSQ presented errors lower than 50%. Radiomic features from f and D* maps were accurate only if computed using DL methods from histogram. High reproducibility (ICC>0.8) was found only for D maps among DL and Bayes methods, whereas features from f and D* maps were less reproducible, with LSQ approach in lower agreement with the others.

CONCLUSIONS

Texture patterns were preserved and correctly estimated only on D maps, except for LSQ approach. We suggest limiting radiomic analysis only to histogram and some texture features from D maps, to histogram features from f maps, and to avoid it on D* maps.

Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney

PURPOSE

To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach.

METHODS

The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting).

RESULTS

The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method.

CONCLUSION

The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.

An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters

Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions

Background

To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging.

Methods

1.5-T DWI data (b = 0, 50, 250, 800 s/mm²) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D₁′ = ADC (50, 800), D₂′ = ADC (250, 800), f₁′ = f (0, 50, 800), f₂′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D₁′ with f₁′ and D₂′ with f₂′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm² (n = 191) and (ii) with suspicious contrast-enhancement (n = 135).

Results

All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm². For task (i), best discrimination was reached for the combination of D₁′ and f₁′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D₁′ (88.0%) and f₁′ (87.4%). For task (ii), best discrimination was reached for single parameter D₁′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D₂′ (88.1%). Adding f₁′ to D₁′ did not improve discrimination.

Conclusions

IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T

PURPOSE

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value.

MATERIALS AND METHODS

Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm² ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm² ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2_p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times.

RESULTS

Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2_p = 100 ms) and 60 % (T2_p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm² was around 8% lower than at b = 0 s/mm² (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2_p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01).

CONCLUSION

The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Perfusion-Diffusion Ratio: A New IVIM Approach in Differentiating Solid Benign and Malignant Primary Lesions of the Liver

Introduction

In order to improve the efficacy of intravoxel incoherent motion (IVIM) parameters in characterising specific tissues, a new concept is introduced: the perfusion–diffusion ratio (PDR), which expresses the relationship between the signal S(b) decline rate as a result of IVIM and the rate of signal S(b) decline due to diffusion. The aim of this study was to investigate this novel approach in the differentiation of solid primary liver lesions. Material and Methods. Eighty-three patients referred for liver MRI between August 2017 and January 2020 with a suspected liver tumour were prospectively examined with the standard liver MRI protocol extended by DWI-IVIM sequence. Patients with no liver lesions, haemangiomas, or metastases were excluded. The final study population consisted of 34 patients with primary solid liver masses, 9 with FNH, 4 with regenerative nodules, 10 with HCC, and 11 with CCC. The PDR coefficient was introduced, defined as the ratio of the rate of signal S(b) decrease due to the IVIM effect to the rate of signal S(b) decrease due to the diffusion process, for b = 0.

Results

No significant differences were found between benign and malignant lesions in the case of IVIM parameters (f, D, or D^∗) and ADC. Significant differences were observed only for PDR, with lower values for malignant lesions (p = 0.03). The ROC analysis yielded an AUC value for PDR equal to 0.74, with a cut-off value of 5.06, sensitivity of 81%, specificity of 77%, and accuracy of 79%.

Conclusion

PDR proved to be more effective than IVIM parameters and ADC in the differentiation of solid benign and malignant primary liver lesions.

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D^∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D^∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline's task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline's advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method's predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

PURPOSE

Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.

METHOD

In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.

RESULTS

In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.

CONCLUSION

IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Preliminary quantitative analysis of vertebral microenvironment changes in type 2 diabetes mellitus using FOCUS IVIM-DWI and IDEAL-IQ sequences

PURPOSE

To explore the application of intravoxel incoherent motion diffusion-weighted imaging(IVIM-DWI) on account of field-of-view optimized and constrained undistorted single shot (FOCUS) and iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation(IDEAL-IQ) sequences in evaluating the vertebral microenvironment changes of type 2 diabetes mellitus(T2DM) patients and the correlation with bone mineral density(BMD).

METHOD

128 T2DM patients (mean age 63.4 ± 5.28 years) underwent both dual-energy X-ray absorptiometry (DEXA) and spine MRI. The FOCUS IVIM-DWI and IDEAL-IQ derived parameters of the vertebral body(L1, L2, L3, L4)were measured on corresponding maps of the lumbar spine. The subjects were divided into 3 groups according to T-scores as follows: normal (n = 37), osteopenia (n = 43), and osteoporosis(n = 48) group.One-way analysis of variance (ANOVA) were used to compare the vertebral parameters(ADC_slow, ADC_fast, f, FF, R2*) among three BMD cohorts.Receiver operating characteristic (ROC) analyses and Spearman's rank correlation were performed to test the diagnostic performance and the correlation between them respectively.

RESULTS

There were significant differences in vertebral ADC_slow, ADC_fast, FF and R2* between the three groups (P < 0.05).Statistically, BMD was moderately negatively correlated with FF (r = -0.584, P < 0.001) and weakly positively with ADC_slow (r = 0.334, P < 0.001), meanwhile moderately positively correlated with R2*(r = 0.509, P < 0.001) and ADC_fast(0.545, P < 0.001).ADC_fast was moderately negatively correlated with FF (r = -0.417, P < 0.001), weakly positively correlated with R2*(0.359, P < 0.001).Compared with the area under the curve (AUC) of ADC_slow, ADC_fast, FF and R2*, the AUC of ADC_fast was higher in identifying between normal and abnormal(osteopenia and osteoporosis), normal from osteopenia, while the AUC of FF was higher in identifying osteopenia from osteoporosis.

CONCLUSIONS

FOCUS IVIM-DWI and IDEAL-IQ of lumbar spine might be useful to evaluate the vertebral microenvironment changes of T2DM patients.

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters

PURPOSE

To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters.

METHODS

Monte-Carlo simulations were generated on a range of SNRs and in different IVIM combinations considering: i) a dense discretization with 24 b-values; ii) a discretization with 9 b-values. A state-of-the-art Bayesian fitting method was adopted. The framework assessed: i) the best model between mono- and bi-exponential, through the BIC index; ii) the fitting accuracy; iii) the power in discriminating two different IVIM parameters distributions of estimated coefficients, using a multivariate test. Exemplificative oncologic cases were also presented.

RESULTS

The bi-exponential fitting was reliable for perfusion fraction higher than 5%, with high accuracy in D estimation, acceptable error for f, but high uncertainty in D*. The discrimination of two distributions is generally feasible if differences in D values (at least 0.3 x10^-3 mm²/s) are present; in the case of similar D values, a minimal difference of 5% in f can be discriminated just in case of balanced sample size and dense b-values discretization, whereas the impact of D* is quite negligible. These results were also supported by clinical examples.

CONCLUSIONS

IVIM model is generally accurate in estimating diffusion, but uncertainties related to perfusion estimation are not negligible and compromise the discrimination power when different populations should be differentiated. The proposed framework should be adopted as interpretative guidelines to better understand when IVIM model applied on real data can provide reliable findings.

Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study

We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%–57% and that of combined DCE- or combined DW-MRI markers to 57%–71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%–93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.

Quantification of Regional Cerebral Blood Flow Using Diffusion Imaging With Phase Contrast

BACKGROUND

The perfusion-related diffusion coefficient obtained from triexponential diffusion analysis is closely correlated with regional cerebral blood flow (rCBF), as assessed by arterial spin labeling (ASL) methods. However, this provides only a semiquantitative measure of rCBF, thereby making absolute rCBF quantification challenging.

PURPOSE

To obtain rCBF in a noninvasive manner using a novel diffusion imaging method with phase contrast (DPC), in which the total CBF from phase-contrast (PC) MRI was utilized to convert perfusion-related diffusion coefficients to rCBF values.

STUDY TYPE

Prospective.

SUBJECTS

Eleven healthy volunteers (nine men and two women; mean age, 23.9 years) participated in this study.

FIELD STRENGTH/SEQUENCE

A 3.0 T, single-shot diffusion echo-planar imaging with multiple b-values (0-3000 s/mm² ), PC-MRI, pulsed continuous ASL, and 3D T₁ -weighted fast field echo.

ASSESSMENT

rCBF and its correlations in the gray matter (GM) and white matter (WM) were compared between DPC and ASL methods. rCBF in the GM and WM and the GM/WM ratio were compared with the literature values obtained using [¹⁵ O]-water positron emission tomography (¹⁵ O-H₂ O PET).

STATISTICAL TESTS

Spearman's correlation coefficient and Wilcoxon signed-rank test were used. Significance was set at P < 0.05.

RESULTS

A significant positive correlation between DPC and ASL in terms of rCBF was observed in GM (R = 0.9), whereas the correlation between the two methods was poor in WM (R = 0.09). The rCBF in GM and WM and the GM/WM ratio obtained using DPC were consistent with the literature values assessed using ¹⁵ O-H₂ O PET. The rCBF value obtained using DPC was significantly higher in the GM and WM than that using ASL.

DATA CONCLUSION

DPC enabled noninvasive quantification of rCBF.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Quantification of intravoxel incoherent motion with optimized b-values using deep neural network

PURPOSE

To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized.

METHOD

A deep neural network (DNN) method is proposed for accurate quantification of IVIM parameters from multiple diffusion-weighted images. In addition, optimal b-values are selected to acquire the multiple diffusion-weighted images. The proposed framework consists of an MRI signal generation part and an IVIM parameter quantification part. Monte-Carlo (MC) simulations were performed to evaluate the accuracy of the IVIM parameter quantification and the efficacy of b-value optimization. In order to analyze the effect of noise on the optimized b-values, simulations were performed with five different noise levels. For in vivo data, diffusion images were acquired with the b-values from four b-values selection methods for five healthy volunteers at 3T MRI system.

RESULTS

Experiment results showed that both the optimization of b-values and the training of DNN were simultaneously performed to quantify IVIM parameters. We found that the accuracies of the perfusion coefficient (D_p ) and perfusion fraction (f) were more sensitive to b-values than the diffusion coefficient (D) was. Furthermore, when the noise level changed, the optimized b-values also changed. Therefore, noise level has to be considered when optimizing b-values for IVIM quantification.

CONCLUSION

The proposed scheme can simultaneously optimize b-values and train DNN to minimize quantification errors of IVIM parameters. The trained DNN can quantify IVIM parameters from the diffusion-weighted images obtained with the optimized b-values.

Renal Diffusion-Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concepts

The specialized function of the kidney is reflected in its unique structure, characterized by juxtaposition of disorganized and ordered elements, including renal glomerula, capillaries, and tubules. The key role of the kidney in blood filtration, and changes in filtration rate and blood flow associated with pathological conditions, make it possible to investigate kidney function using the motion of water molecules in renal tissue. Diffusion-weighted imaging (DWI) is a versatile modality that sensitizes observable signal to water motion, and can inform on the complexity of the tissue microstructure. Several DWI acquisition strategies are available, as are different analysis strategies, and models that attempt to capture not only simple diffusion effects, but also perfusion, compartmentalization, and anisotropy. This chapter introduces the basic concepts of DWI alongside common acquisition schemes and models, and gives an overview of specific DWI applications for animal models of renal disease.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.

Untangling the diffusion signal using the phasor transform

Separating the decay signal from diffusion-weighted scans into two or more components can be challenging. The phasor technique is well established in the field of optical microscopy for visualization and separation of fluorescent dyes with different lifetimes. The use of the phasor technique for separation of diffusion-weighted decay signals was recently proposed. In this study, we investigate the added value of this technique for fitting decay models and visualization of decay rates. Phasor visualization was performed in five glioblastoma patients. Using simulations, the influence of incorrect diffusivity values and of the number of b-values on fitting a three-component model with fixed diffusivities (dubbed "unmixing") was investigated for both a phasor-based fit and a linear least squares (LLS) fit. Phasor-based intravoxel incoherent motion (IVIM) fitting was compared with nonlinear least squares (NLLS) and segmented fitting (SF) methods in terms of accuracy and precision. The distributions of the parameter estimates of simulated data were compared with those obtained in a healthy volunteer. In the phasor visualizations of two glioblastoma patients, a cluster of points was observed that was not seen in healthy volunteers. The identified cluster roughly corresponded to the enhanced edge region of the tumor of two glioblastoma patients visible on fluid-attenuated inversion recovery (FLAIR) images. For fitting decay models the usefulness of the phasor transform is less pronounced, but the additional knowledge gained from the geometrical configuration of phasor space can aid fitting routines. This has led to slightly improved fitting results for the IVIM model: phasor-based fitting yielded parameter maps with higher precision than the NLLS and SF methods for parameters f and D (interquartile range [IQR] for f: NLLS 27, SF 12, phasor 5.7%; IQR for D: NLLS 0.28, SF 0.18, phasor 0.10 μm² /s). For unmixing, LLS fitting slightly but consistently outperformed phasor-based fitting in all of the tested scenarios.

Investigation of intravoxel incoherent motion tensor imaging for the characterization of the in vivo human heart

PURPOSE

To investigate intravoxel incoherent motion (IVIM) tensor imaging of the in vivo human heart and elucidate whether the estimation of IVIM tensors is affected by the complexity of pseudo-diffusion components in myocardium.

METHODS

The cardiac IVIM data of 10 healthy subjects were acquired using a diffusion weighted spin-echo echo-planar imaging sequence along 6 gradient directions with 10 b values (0~400 s/mm² ). The IVIM data of left ventricle myocardium were fitted to the IVIM tensor model. The complexity of myocardial pseudo-diffusion components was reduced through exclusion of low b values (0 and 5 s/mm² ) from the IVIM curve-fitting analysis. The fractional anisotropy, mean fraction/mean diffusivity, and Westin measurements of pseudo-diffusion tensors (f_p and D*) and self-diffusion tensor (D), as well as the angle between the main eigenvector of f_p (or D*) and that of D, were computed and compared before and after excluding low b values.

RESULTS

The fractional anisotropy values of f_p and D* without low b value participation were significantly higher (P < .001) than those with low b value participation, but an opposite trend was found for the mean fraction/diffusivity values. Besides, after removing low b values, the angle between the main eigenvector of f_p (or D*) and that of D became small, and both f_p and D* tensors presented significant decrease of spherical components and significant increase of linear components.

CONCLUSION

The presence of multiple pseudo-diffusion components in myocardium indeed influences the estimation of IVIM tensors. The IVIM tensor model needs to be further improved to account for the complexity of myocardial microcirculatory network and blood flow.

Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b-values

PURPOSE

Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion-related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b-values.

METHODS

Bayesian shrinkage prior (BSP) fitting method and different low b-value distributions were used to estimate IVIM parameters (diffusion D, pseudo-diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor.

RESULTS

In tumor regions, the value of D* tended to be decreased when the number of low b-values was insufficient, especially with LSQ. BSP required less low b-values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F.

CONCLUSIONS

Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b-values than LSQ for the parameter estimation.

Optimization of intravoxel incoherent motion (IVIM): variability of parameters measurements using a reduced distribution of b values for breast tumors analysis

OBJECTIVES

This study aimed to examine the variability of intravoxel incoherent motion measurements acquired from reduced distributions of b values for breast tumors analysis.

MATERIALS AND METHODS

The investigations were carried out on twenty-four patients with diagnosed breast tumors. A conventional unenhanced MRI and various IVIM series preset with different distributions of b values (0-1000 s/mm²) were performed. We assessed the variability in D_slow, D_fast, and PF measurements for different distributions of 9 to 4 b values compared with the IVIM metrics for 10 b values using Wilcoxon-Signed rank test. The data was statistically significant at P < 0.05.

RESULTS

The results showed no significant variation in the estimations of IVIM parameters in patients. However, the measurements acquired with the combination of 5 b values Showed some variation in D_fast (P = 0.028) compared with 10 b values. The data showed high wCVs in the measurements acquired using the reduced set of 6 b values for D_slow and PF and with the combination of 7 b values for D_fast. There were inconsistencies noticed in the measurements acquired from malignant tumors using reduced distributions of b values (9 b values-4 b values). However, the set of 4 b values displayed the lowest wCVs for both benign and malignant datasets. We also observed unsystematic correlations among different combinations of b values in the categories of IVIM parameters.

CONCLUSION

There was no relevant variation in the parameters measurements irrespective of the number of b values used. Reduced distributions of b values may find use in estimations of IVIM parameters for breast lesions analysis.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations

PURPOSE

To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm² in benign and malignant breast lesions.

METHODS

Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm² ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated.

RESULTS

The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at b_max = 600 s/mm² already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction.

CONCLUSION

Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI

Objectives

Standardization is an important milestone in the validation of DWI-based parameters as imaging biomarkers for renal disease. Here, we propose technical recommendations on three variants of renal DWI, monoexponential DWI, IVIM and DTI, as well as associated MRI biomarkers (ADC, D, D*, f, FA and MD) to aid ongoing international efforts on methodological harmonization.

Materials and methods

Reported DWI biomarkers from 194 prior renal DWI studies were extracted and Pearson correlations between diffusion biomarkers and protocol parameters were computed. Based on the literature review, surveys were designed for the consensus building. Survey data were collected via Delphi consensus process on renal DWI preparation, acquisition, analysis, and reporting. Consensus was defined as ≥ 75% agreement.

Results

Correlations were observed between reported diffusion biomarkers and protocol parameters. Out of 87 survey questions, 57 achieved consensus resolution, while many of the remaining questions were resolved by preference (65–74% agreement). Summary of the literature and survey data as well as recommendations for the preparation, acquisition, processing and reporting of renal DWI were provided.

Discussion

The consensus-based technical recommendations for renal DWI aim to facilitate inter-site harmonization and increase clinical impact of the technique on a larger scale by setting a framework for acquisition protocols for future renal DWI studies. We anticipate an iterative process with continuous updating of the recommendations according to progress in the field.

Electronic supplementary material

The online version of this article (10.1007/s10334-019-00790-y) contains supplementary material, which is available to authorized users.

Impact of b-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects

Purpose:

Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers.

Methods:

Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered ‘regular’ due to its close association with that used in other studies, and the other considered ‘optimized’ according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI.

Results:

Maps with fewer speckles were obtained with the ‘optimized’ distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the ‘optimized’ case.

Conclusion:

Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm

Diffusion functional magnetic resonance imaging (dfMRI) is a promising technique to map functional activations by acquiring diffusion‐weighed spin‐echo images. In previous studies, dfMRI showed higher spatial accuracy at activation mapping compared to classic functional MRI approaches. However, it remains unclear whether dfMRI measures result from changes in the intracellular/extracellular environment, perfusion, and/or T₂ values. We designed an acquisition/quantification scheme to disentangle such effects in the motor cortex during a finger‐tapping paradigm. dfMRI was acquired at specific diffusion weightings to selectively suppress perfusion and free‐water diffusion, then time series of the apparent diffusion coefficient (ADC‐fMRI) and of intravoxel incoherent motion (IVIM) effects were derived. ADC‐fMRI provided ADC estimates sensitive to changes in perfusion and free‐water volume, but not to T₂/T₂* values. With IVIM modeling, we isolated the perfusion contribution to ADC, while suppressing T₂ effects. Compared to conventional gradient‐echo blood oxygenation level‐dependent fMRI, activation maps obtained with dfMRI and ADC‐fMRI had smaller clusters, and the spatial overlap between the three techniques was below 50%. Increases of perfusion fractions were observed during task in both dfMRI and ADC‐fMRI activations. Perfusion effects were more prominent with ADC‐fMRI than with dfMRI but were significant in less than 25% of activation regions. IVIM modeling suggests that the sensitivity to task of dfMRI derives from a decrease of intracellular/extracellular diffusion and an increase of the pseudo‐diffusion signal fraction, leading to different, more confined spatial activation patterns compared to classic functional MRI.

Monitoring the Early Antiproliferative Effect of the Analgesic-Antitumor Peptide, BmK AGAP on Breast Cancer Using Intravoxel Incoherent Motion With a Reduced Distribution of Four b-Values

Background: The present study aimed to investigate the possibility of using intravoxel incoherent motion (IVIM) diffusion magnetic resonance imaging (MRI) to quantitatively assess the early therapeutic effect of the analgesic–antitumor peptide BmK AGAP on breast cancer and also evaluate the medical value of a reduced distribution of four b-values.

Methods: IVIM diffusion MRI using 10 b-values and 4 b-values (0–1,000 s/mm²) was performed at five different time points on BALB/c mice bearing xenograft breast tumors treated with BmK AGAP. Variability in Dslow, Dfast, PF, and ADC derived from the set of 10 b-values and 4 b-values was assessed to evaluate the antitumor effect of BmK AGAP on breast tumor.

Results: The data showed that PF values significantly decreased in rBmK AGAP-treated mice on day 12 (P = 0.044). PF displayed the greatest AUC but with a poor medical value (AUC = 0.65). The data showed no significant difference between IVIM measurements acquired from the two sets of b-values at different time points except in the PF on the day 3. The within-subject coefficients of variation were relatively higher in Dfast and PF. However, except for a case noticed on day 0 in PF measurements, the results indicated no statistically significant difference at various time points in the rBmK AGAP-treated or the untreated group (P < 0.05).

Conclusion: IVIM showed poor medical value in the early evaluation of the antiproliferative effect of rBmK AGAP in breast cancer, suggesting sensitivity in PF. A reduced distribution of four b-values may provide remarkable measurements but with a potential loss of accuracy in the perfusion-related parameter PF.

Optimization of b-value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting

PURPOSE

Intravoxel incoherent motion (IVIM) modeling for estimation of the diffusion coefficient (D) and perfusion fraction (f) is increasingly popular, but no consensus on standard protocols exists. This study provides a framework for optimization of b-value schemes for reduced estimation uncertainty of D and f from segmented model fitting.

THEORY

Analytical expressions for uncertainties of D and f from segmented model fitting were derived as Cramer-Rao lower bounds (CRLBs).

METHODS

Optimized b-value schemes were obtained for 3 to 12 acquisitions and in the limit of infinitely many acquisitions through constrained minimization of the CRLBs, with b-values constrained to be 0 or 200 to 800 s/mm² . The optimized b-value scheme with eight acquisitions was compared with b-values linearly distributed in the allowed range using simulations and in vivo liver data from seven healthy volunteers.

RESULTS

All optimized b-value schemes contained exactly three unique b-values regardless of the total number of acquisitions (0, 200, and 800 s/mm² ) with repeated acquisitions distributed approximately as 1:2:2. Compared with linearly distributed b-values, the variability of estimates of D and f was reduced by approximately 30% as seen both in simulations and in repeated in vivo measurements.

CONCLUSION

The uncertainty of IVIM D and f estimates can be reduced by the use of optimized b-value schemes.

Estimation of intravoxel incoherent motion parameters using low b-values

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise.

Grading and proliferation assessment of diffuse astrocytic tumors with monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging and diffusion kurtosis imaging

PURPOSE

To compare the main parameters derived from monoexponential, biexponential and stretched-exponential diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) with respect to diagnostic performance for tumor grading and proliferation assessment in diffuse astrocytic tumors (DATs).

MATERIALS AND METHODS

Fifty-eight pathologically confirmed DAT patients who underwent DWI and DKI on a 3-T scanner were prospectively collected and retrospectively reviewed. Measurements including the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D^*), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), fractional anisotropy (FA), and mean kurtosis (MK) were compared between tumor grades (Ⅱ, Ⅲ, and Ⅳ) by using a Jonckheere-Terpstra test. Receiver operating characteristic (ROC) curves were used to assess the diagnostic efficacy of these parameters. Spearman's rho with the Ki-67 labeling index (LI) was calculated for each parameter.

RESULTS

MK values differed significantly between all DAT subtypes and increased with grade. The ADC, D, f, DDC, α and MD values were significantly higher in grade Ⅱ tumors than in grade Ⅲ/Ⅳ tumors. D^* values were significantly lower in grade Ⅱ tumors than in grade Ⅳ tumors (all P < 0.05). In discriminating between grade Ⅱ and Ⅲ tumors, α, MK, MD, D and f had significantly greater area under the ROC curve (AUC) values than D^* and FA (0.927, 0.901, 0.896, 0.895, and 0.889, respectively vs 0.659 and 0.598, respectively, P < 0.05). In discriminating between grade Ⅲ and Ⅳ tumors, only MK demonstrated acceptable discrimination (AUC = 0.711). MK and D showed a strong correlation with the Ki-67 LI (ρ = 0.791 and -0.789, respectively, P < 0.001). D^*, f, MD, ADC, DDC and α showed a moderate correlation (|ρ| ranged from 0.415 to 0.698, P < 0.05).

CONCLUSION

MK and D have considerable potential to predict the degree of proliferation of DATs. MK could effectively characterize microstructural changes throughout the malignant transformation of DATs and provided useful complementary information for grading.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Triggered intravoxel incoherent motion MRI for the assessment of calf muscle perfusion during isometric intermittent exercise

The main aim of this paper was to propose triggered intravoxel incoherent motion (IVIM) imaging sequences for the evaluation of perfusion changes in calf muscles before, during and after isometric intermittent exercise. Twelve healthy volunteers were involved in the study. The subjects were asked to perform intermittent isometric plantar flexions inside the MRI bore. MRI of the calf muscles was performed on a 3.0 T scanner and diffusion-weighted (DW) images were obtained using eight different b values (0 to 500 s/mm² ). Acquisitions were performed at rest, during exercise and in the subsequent recovery phase. A motion-triggered echo-planar imaging DW sequence was implemented to avoid movement artifacts. Image quality was evaluated using the average edge strength (AES) as a quantitative metric to assess the motion artifact effect. IVIM parameters (diffusion D, perfusion fraction f and pseudo-diffusion D*) were estimated using a segmented fitting approach and evaluated in gastrocnemius and soleus muscles. No differences were observed in quality of IVIM images between resting state and triggered exercise, whereas the non-triggered images acquired during exercise had a significantly lower value of AES (reduction of more than 20%). The isometric intermittent plantar-flexion exercise induced an increase of all IVIM parameters (D by 10%; f by 90%; D* by 124%; fD* by 260%), in agreement with the increased muscle perfusion occurring during exercise. Finally, IVIM parameters reverted to the resting values within 3 min during the recovery phase. In conclusion, the IVIM approach, if properly adapted using motion-triggered sequences, seems to be a promising method to investigate muscle perfusion during isometric exercise.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

BACKGROUND

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.

OBJECTIVE

The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.

METHODS

The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.

RESULTS

Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.

CONCLUSIONS

MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman’s rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging

PURPOSE

An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM-DTI) model was proposed, which accounts for both anisotropic pseudo-diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM-DTI approach for simultaneous diffusion and pseudo-diffusion tensor imaging.

METHODS

Conventional IVIM estimation methods can be broadly divided into two-step (diffusion and pseudo-diffusion estimated separately) and one-step (diffusion and pseudo-diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM-DTI model. An improved one-step method based on damped Gauss-Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data.

RESULTS

The one-step damped Gauss-Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM-DTI parameters compared to the other methods.

CONCLUSION

One-step estimation using damped Gauss-Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo-diffusion tensor imaging using IVIM-DTI model. Magn Reson Med 79:2367-2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.